The AI-green paradox: mitigating algorithmic greenwashing through intersectional leadership in the twin transition

Purpose As organizations leverage advanced digital technologies to drive sustainability – a convergence recognized as the “twin transition” – they face heterogeneous and conditional risks. This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes. It aims to provide executives with a “Responsible Intersectional Techno-Leadership” (RITL) playbook to govern these socio-technical complexities. Design/methodology/approach Rather than a general literature review, this conceptual paper employs a targeted synthesis of recent discourse across twin transition dynamics, AI ethics, and strategic management. It integrates intersectionality theory into upper echelons logic to construct an actionable, context-aware leadership methodology suitable for diverse global environments. Findings The analysis identifies a critical governance blind spot: the integration of digital and green strategies is not uniformly beneficial. Without intersectional oversight, AI tools can reproduce systemic historical inequalities, leading to what is formalized here as “algorithmic greenwashing.” The proposed RITL framework offers three operational levers for executives: Cognitive Audits (Strategic Awareness), Intersectional Talent Architecture (Structural Participation), and The Accountability Loop (Process Governance). Practical implications To address the operational ambiguity of the twin transition, the paper provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers. It offers concrete mechanisms to audit their AI supply chains and foster intersectional STEM ecosystems, ensuring that digital sustainability efforts mitigate, rather than exacerbate, corporate ESG risks across varying organizational contexts. Originality/value Moving beyond the established consensus that digitalization and sustainability are linked, this paper contributes by problematizing the twin transition. It reframes intersectional inclusion not as a compliance cost, but as a strategic asset and risk-management firewall, offering a nuanced approach to AI governance that ensures sustainable futures are equitable.